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Energies 2016, 9(10), 797; doi:10.3390/en9100797

Adaptive TrimTree: Green Data Center Networks through Resource Consolidation, Selective Connectedness and Energy Proportional Computing

1
Department of Electrical Engineering, National University of Computer & Emerging Sciences, Lahore 54700, Pakistan
2
Department of Computer Science, Dhofar University, Salalah 211, Oman
*
Author to whom correspondence should be addressed.
Received: 28 May 2016 / Revised: 8 August 2016 / Accepted: 27 September 2016 / Published: 7 October 2016
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Abstract

A data center is a facility with a group of networked servers used by an organization for storage, management and dissemination of its data. The increase in data center energy consumption over the past several years is staggering, therefore efforts are being initiated to achieve energy efficiency of various components of data centers. One of the main reasons data centers have high energy inefficiency is largely due to the fact that most organizations run their data centers at full capacity 24/7. This results into a number of servers and switches being underutilized or even unutilized, yet working and consuming electricity around the clock. In this paper, we present Adaptive TrimTree; a mechanism that employs a combination of resource consolidation, selective connectedness and energy proportional computing for optimizing energy consumption in a Data Center Network (DCN). Adaptive TrimTree adopts a simple traffic-and-topology-based heuristic to find a minimum power network subset called ‘active network subset’ that satisfies the existing network traffic conditions while switching off the residual unused network components. A ‘passive network subset’ is also identified for redundancy which consists of links and switches that can be required in future and this subset is toggled to sleep state. An energy proportional computing technique is applied to the active network subset for adapting link data rates to workload thus maximizing energy optimization. We have compared our proposed mechanism with fat-tree topology and ElasticTree; a scheme based on resource consolidation. Our simulation results show that our mechanism saves 50%–70% more energy as compared to fat-tree and 19.6% as compared to ElasticTree, with minimal impact on packet loss percentage and delay. Additionally, our mechanism copes better with traffic anomalies and surges due to passive network provision. View Full-Text
Keywords: data center network; energy consumption; resource consolidation; selective connectedness; energy proportional computing; adaptive link rate data center network; energy consumption; resource consolidation; selective connectedness; energy proportional computing; adaptive link rate
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zafar, S.; Chaudhry, S.A.; Kiran, S. Adaptive TrimTree: Green Data Center Networks through Resource Consolidation, Selective Connectedness and Energy Proportional Computing. Energies 2016, 9, 797.

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